AI used to be a software story, and nowNow it is an infrastructure story. Every chatbot response, enterprise AI workflow, image model, agentic system, and machine-learning deployment depends on a physical, real-world stack: chips, servers, storage, networking, data centers, cooling, power, Cloud platforms, and the companies that connect all of it together. That is why the AI ETF conversation is moving beyond “who has the smartest model?” and toward “who is building the machine room for the AI economy?”
First, the obligatory disclaimer
Before we go any further: this is not investment advice. No, seriously. ETF performance can rise and fall, capital is at risk, and investors should always do their own research or speak to a qualified financial adviser. YMMV, FAFO, etc.
Still, I would be lying if I said I had never dabbled. I suspect I am not the only person who has, at some point, opened one of those investing apps and played the slightly dangerous and somewhat thrilling game of cyber-Monopoly with real-world money. I will not tell you how it ended, but let’s just say it made the disclaimer above feel quite necessary.
Big tech is spending big on AI
That is also why the current excitement around AI infrastructure ETFs is worth looking at carefully. The world’s biggest technology companies are spending massive amounts on AI and Cloud expansion. Reuters recently reported that combined spending from major tech giants is now expected to exceed $700 billion this year, up from around $600 billion previously. Goldman Sachs has gone even bigger, estimating roughly $7.6 trillion of AI-related capital expenditure between 2026 and 2031 across computing, data centers, and power. That is not a side quest—it’s the new main storyline.
At CloudFest, we have already covered how data center systems spending is expected to surge in 2026, driven by AI-optimized servers, accelerated computing, networking upgrades, cooling, and Cloud capacity. The “AI boom” is not just creating demand for GPUs. It is creating demand for the entire digital-industrial supply chain that AI needs.
Investors are looking beyond the best “AI ETF”
That explains the rise of searches around “AI ETF,” “AI infrastructure ETF,” “semiconductor ETF,” “data center ETF,” “cloud ETF,” and “cloud computing ETF.” (Come on, people, capitalize “Cloud”!) Investors are not only looking for the best AI ETF in the abstract. They are trying to understand which funds capture the companies powering AI at scale.
Broad AI ETFs cover the entire value chain
Source: iShares.com
Some of the largest and most visible funds sit in slightly different parts of the stack. The iShares A.I. Innovation and Tech Active ETF had around $13.96 billion in net assets as of early June 2026, and targets companies across the AI value chain, including infrastructure, intelligence, apps, and services. Global X Artificial Intelligence & Technology ETF reported $10.21 billion in net assets also as of the beginning of June, with large exposure to information technology and holdings including memory, chip, networking, and Cloud-related names.
Semiconductors are AI’s engine room
Then there are the semiconductor giants. VanEck Semiconductor ETF reported $66.36 billion in total net assets as of June 10, 2026, while iShares Semiconductor ETF reported $38.68 billion in net assets on the same date. These are not “AI infrastructure ETFs” in the narrowest sense, but semiconductors are the high-performance engine room of artificial intelligence. No chips, no training. No accelerators, no inference. No memory, networking, or equipment, no AI data center build-out.
AI is making Cloud more strategic
Cloud computing ETFs also matter. First Trust Cloud Computing ETF, for example, had about $2.88 billion in total net assets as of June 2026 and tracks companies involved in the cloud computing industry. For CloudFest readers, this is where the story gets especially interesting. AI is not replacing Cloud. It is making Cloud more strategic, more capital-intensive, and more dependent on the infrastructure providers who can deliver performance, availability, security, sovereignty, and cost control.
How have these ETFs performed?
For readers comparing this with their own portfolios, recent performance is worth a quick look. As a broad benchmark, VOO, Vanguard’s S&P 500 ETF, showed roughly a 30% one-year return in early June 2026. Against that, AI infrastructure exposure has been uneven but often stronger: AIQ reported a 27.47% one-year NAV return at quarter-end, while chip-heavy funds such as SMH and SOXX showed much sharper gains in early June snapshots. SKYE, the Cloud computing ETF, was more moderate, with recent one-year figures generally in the high-teens to low-20s range. BAI is newer, so its two-year comparison is limited.
What are investors actually buying?
The holdings matter as much as the ETF name. SMH recently had Nvidia at about 14%, Taiwan Semiconductor around 9%, Micron around 8%, Intel around 8%, AMD around 7%, and Broadcom around 6%. SOXX also offers concentrated semiconductor exposure, with Micron, AMD, Broadcom, Nvidia, and Intel among its largest names. AIQ and BAI are broader AI-stack funds, with exposure across chips, platforms, Cloud, and data infrastructure. SKYY is different again, with names such as DigitalOcean, Oracle, Nutanix, Dell, and IBM, making it more of a Cloud infrastructure play than a pure chip bet.
Source: SMH investing holdings
Power and cooling join the investment thesis
Power is becoming part of the investment thesis too. The International Energy Agency projects global data center electricity consumption to roughly double to around 945 TWh by 2030, while electricity consumption from accelerated servers, mainly driven by AI, is projected to grow by 30% annually in its base case. In other words, the AI infrastructure opportunity is not only about silicon. It is also about grids, cooling systems, energy efficiency, colocation strategy, and where the next generation of data centers can actually be built.
Hot sectors can still overheat
So, are AI infrastructure ETFs the hottest market sector? They are certainly among the hottest narratives, but hot sectors can overheat. The strongest funds are not automatically the ones with “AI” in the name, and the biggest AUM (Assets Under Management) does not guarantee the best future return. Concentration risk, valuations, fees, geography, index methodology, liquidity, and overlap with existing holdings all matter.
Which parts of the stack will capture the value?
The smarter question is not “Which AI ETF will win?” But rather, which parts of the AI infrastructure stack do you believe will keep capturing value as AI moves from hype to production?
For the Cloud, hosting, and internet infrastructure community, that question is not theoretical. It is the business landscape forming right now, one rack, GPU cluster, fiber route, cooling loop, and Cloud deployment at a time.
